Autoaggregation in <i>Streptococcus intermedius</i> is driven by the Pel polysaccharide
Bibliographic record
Abstract
ABSTRACT The Streptococcus Milleri Group (SMG) comprising of Streptococcus intermedius, anginosus and constellatus are commensal bacteria commonly found in healthy individuals. These bacteria are increasingly being recognized as opportunistic pathogens that can cause purulent infections at sterile body sites and have also been identified in the sputum of individuals with cystic fibrosis. Although the mechanisms of conversion to opportunistic pathogens are not well understood, auto-aggregation is a key driver of biofilm adhesion and cohesion in many Streptococci and Staphylococci. Here, we identify a gene cluster in the S. intermedius genome with significant homology to the pel operons in Bacillus cereus and Pseudomonas aeruginosa , which are required for Pel exopolysaccharide production and biofilm formation in these species. Characterization of a panel of clinical S. intermedius strains identified a range of aggregating phenotypes. Analysis of the pel operon in the hyper-aggregating C1365 strain revealed that each of the canonical pelDEA DA FG genes, but not the four additional genes are required for aggregation. Further, we demonstrate that C1365 produces a GalNAc-rich exopolysaccharide and that aggregates can be disrupted by the α1,4 N- acetylgalactosaminidases, PelA and Sph3, but not other glycoside hydrolases, proteinase K or DNase I. Using an abscess model of mouse infection, we show that Pel driven aggregation leads to longer lasting infections, and that lack of Pel allows for the bacteria to be cleared more effectively. The polymer also affects how the bacteria interacts with the host immune system. Collectively, our data suggest that the pel operon has relevancy to S. intermedius pathogenicity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".